Proceedings of the 21st ACM international conference on Information and knowledge management最新文献_第8页

Adapt: adaptive database schema design for multi-tenant applications Adapt:针对多租户应用程序的自适应数据库模式设计

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398601

Jiacai Ni, Guoliang Li, Jun Zhang, Lei Li, Jianhua Feng

{"title":"Adapt: adaptive database schema design for multi-tenant applications","authors":"Jiacai Ni, Guoliang Li, Jun Zhang, Lei Li, Jianhua Feng","doi":"10.1145/2396761.2398601","DOIUrl":"https://doi.org/10.1145/2396761.2398601","url":null,"abstract":"Multi-tenant data management is a major application of software as a Service (SaaS). Many companies outsource their data to a third party which hosts a multi-tenant database system to provide data management service. The system should have high performance, low space and excellent scalability. One big challenge is to devise a high-quality database schema. Independent Tables Shared Instances and Shared Tables Shared Instances are two state-of-the-art methods. However, the former has poor scalability, while the latter achieves good scalability at the expense of poor performance and high space overhead. In this paper, we trade-off between the two methods and propose an adaptive database schema design approach to achieve good scalability and high performance with low space. To this end, we identify the important attributes and use them to generate a base table. For other attributes, we construct supplementary tables. We propose a cost-based model to adaptively generate the tables above. Our method has the following advantages. First, our method achieves high scalability. Second, our method can trade-off performance and space requirement. Third, our method can be easily applied to existing databases (e.g., MySQL) with minor revisions. Fourth, our method can adapt to any schemas and query workloads. Experimental results show our method achieves high performance and good scalability with low space and outperforms state-of-the-art method.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130740867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Recency-sensitive model of web page authority 网页权限的最近敏感模型

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398708

M. Zhukovskiy, D. Vinogradov, Gleb Gusev, P. Serdyukov, A. Raigorodskii

引用次数: 3

Top-N recommendation through belief propagation 通过信念传播进行Top-N推荐

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398636

Jiwoon Ha, Soon-Hyoung Kwon, Sang-Wook Kim, C. Faloutsos, Sunju Park

引用次数: 29

PathRank: a novel node ranking measure on a heterogeneous graph for recommender systems PathRank:一种基于异构图的推荐系统节点排序方法

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398488

S. Lee, Sungchan Park, Minsuk Kahng, Sang-goo Lee

引用次数: 35

Enabling direct interest-aware audience selection 支持直接的兴趣感知受众选择

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2396836

A. Fuxman, A. Kannan, Z. Li, Panayiotis Tsaparas

引用次数: 1

A constraint to automatically regulate document-length normalisation 自动调节文档长度规范化的约束

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398662

Ronan Cummins, C. O'Riordan

引用次数: 16

Learning to rank by aggregating expert preferences 学习通过汇总专家偏好来排序

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2396868

M. Volkovs, H. Larochelle, R. Zemel

引用次数: 23

On the inference of average precision from score distributions 从分数分布推断平均精度

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398660

Ronan Cummins

引用次数: 5

Learning from mistakes: towards a correctable learning algorithm 从错误中学习:走向一个可纠正的学习算法

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398546

Karthik Raman, K. Svore, Ran Gilad-Bachrach, C. Burges

引用次数: 4

CONSENTO: a new framework for opinion based entity search and summarization CONSENTO:一个基于意见的实体搜索和总结的新框架

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398547

Jaehoon Choi, Donghyeon Kim, Seongsoon Kim, Junkyu Lee, Sangrak Lim, Sunwon Lee, Jaewoo Kang

{"title":"CONSENTO: a new framework for opinion based entity search and summarization","authors":"Jaehoon Choi, Donghyeon Kim, Seongsoon Kim, Junkyu Lee, Sangrak Lim, Sunwon Lee, Jaewoo Kang","doi":"10.1145/2396761.2398547","DOIUrl":"https://doi.org/10.1145/2396761.2398547","url":null,"abstract":"Search engines have become an important decision making tool today. Decision making queries are often subjective, such as \"a good birthday present for my girlfriend\", \"best action movies in 2010\", to name a few. Unfortunately, such queries may not be answered properly by conventional search systems. In order to address this problem, we introduce Consento, a consensus search engine designed to answer subjective queries. Consento performs segment indexing, as opposed to document indexing, to capture semantics from user opinions more precisely. In particular, we define a new indexing unit, Maximal Coherent Semantic Unit (MCSU). An MCSU represents a segment of a document, which captures a single coherent semantic. We also introduce a new ranking method, called ConsensusRank that counts online comments referring to an entity as a weighted vote. In order to validate the efficacy of the proposed framework, we compare Consento with standard retrieval models and their recent extensions for opinion based entity ranking. Experiments using movie and hotel data show the effectiveness of our framework.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126493905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10